{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:58:06.468311",
     "start_time": "2016-08-17T07:58:04.883583"
    },
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pprint\n",
    "from cohorts.functions import *\n",
    "import lifelines as ll\n",
    "import patsy\n",
    "import functools\n",
    "import survivalstan\n",
    "from cohorts.utils import strip_column_names\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## set seeds for stan & rngs, to aid in reproducibility\n",
    "## (note: only reproducible within a machine, not across machines)\n",
    "\n",
    "seed = 91038753\n",
    "import random\n",
    "random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:58:06.519332",
     "start_time": "2016-08-17T07:58:06.499244"
    },
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from utils import data\n",
    "from utils import paper\n",
    "from utils.extra_functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# prep data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:58:11.840853",
     "start_time": "2016-08-17T07:58:06.520959"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "{'dataframe_hash': 7698303973572390439,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort(join_with=[\"ensembl_coverage\",\"tcr_peripheral_a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:21.487992",
     "start_time": "2016-08-17T07:58:11.931783"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    }
   ],
   "source": [
    "def tcell_fraction(row):\n",
    "    return row[\"T-cell fraction\"]\n",
    "\n",
    "def peripheral_clonality_a(row):\n",
    "    return row['Clonality']\n",
    "\n",
    "cols, d = cohort.as_dataframe([snv_count,\n",
    "                               missense_snv_count,\n",
    "                               neoantigen_count,\n",
    "                               expressed_exonic_snv_count,\n",
    "                               expressed_missense_snv_count,\n",
    "                               expressed_neoantigen_count,\n",
    "                               exonic_snv_count,\n",
    "                               peripheral_clonality_a,\n",
    "                               tcell_fraction,\n",
    "                               ],\n",
    "                              rename_cols=True,\n",
    "                              return_cols=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:21.537104",
     "start_time": "2016-08-17T07:59:21.513702"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['snv_count',\n",
       " 'missense_snv_count',\n",
       " 'neoantigen_count',\n",
       " 'expressed_exonic_snv_count',\n",
       " 'expressed_missense_snv_count',\n",
       " 'expressed_neoantigen_count',\n",
       " 'exonic_snv_count',\n",
       " 'peripheral_clonality_a',\n",
       " 'tcell_fraction']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## construct/rescale variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:21.559557",
     "start_time": "2016-08-17T07:59:21.539192"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## add/modify count variables\n",
    "d['nonexonic_snv_count'] = d.snv_count - d.exonic_snv_count\n",
    "cols.append('nonexonic_snv_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.132709",
     "start_time": "2016-08-17T07:59:22.081884"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## create 'observed', log-transformed & centered versions of variables (not normalized by MB)\n",
    "for col in cols:\n",
    "    observed_col = 'observed_{}'.format(col)\n",
    "    log_col = 'log_{}'.format(col)\n",
    "    log_col_centered = 'log_{}_centered'.format(col)\n",
    "    log_col_rescaled = 'log_{}_rescaled'.format(col)\n",
    "    d[observed_col] = d[col]*d['mb']\n",
    "    d[log_col] = np.log1p(d[observed_col])\n",
    "    d[log_col_centered] = d[log_col] - np.mean(d[log_col])\n",
    "    d[log_col_rescaled] = d[log_col_centered]/np.std(d[log_col_centered])\n",
    "\n",
    "## save key vars in list for future use\n",
    "vars_centered = ['log_{}_centered'.format(col) for col in cols]\n",
    "vars_rescaled = ['log_{}_rescaled'.format(col) for col in cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.174374",
     "start_time": "2016-08-17T07:59:22.134744"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## construct new variables for key ratios / comparisons\n",
    "\n",
    "# what proportion of X are expressed?\n",
    "d['exonic_expression_ratio'] = d.expressed_exonic_snv_count / d.exonic_snv_count\n",
    "d['missense_expression_ratio'] = d.expressed_missense_snv_count / d.missense_snv_count\n",
    "d['neoantigen_expression_ratio'] = d.expressed_neoantigen_count / d.neoantigen_count\n",
    "\n",
    "# d['expressed_missense2snv_ratio'] = d.expressed_missense_snv_count / d.expressed_snv_count\n",
    "d['missense2exonic_snv_ratio'] = d.missense_snv_count / d.exonic_snv_count\n",
    "d['expressed_neoantigen2missense_ratio'] = d.expressed_neoantigen_count / d.expressed_missense_snv_count\n",
    "\n",
    "extra_cols = ['missense_expression_ratio','neoantigen_expression_ratio', 'exonic_expression_ratio', 'missense2exonic_snv_ratio','expressed_neoantigen2missense_ratio']\n",
    "## create recentered versions of ratios\n",
    "for col in extra_cols:\n",
    "    col_centered = '{}_centered'.format(col)\n",
    "    col_rescaled = '{}_rescaled'.format(col)\n",
    "    d[col_centered] = d[col] - np.mean(d[col])\n",
    "    d[col_rescaled] = d[col_centered]/np.std(d[col_centered])\n",
    "\n",
    "## append extra-cols to key var lists\n",
    "vars_centered.extend(['{}_centered'.format(col) for col in extra_cols])\n",
    "vars_rescaled.extend(['{}_rescaled'.format(col) for col in extra_cols])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## center variables by mean within PD-L1 group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.220909",
     "start_time": "2016-08-17T07:59:22.199735"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## identify list of variables to center\n",
    "metrics = list(cols)\n",
    "metrics.extend(extra_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "metrics2 = list(metrics)\n",
    "metrics2.extend(['pd_l1'])\n",
    "assert(not 'pd_l1' in metrics)\n",
    "log_metrics2 = ['log_{}'.format(var) for var in metrics2]\n",
    "metrics2.extend(log_metrics2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.242582",
     "start_time": "2016-08-17T07:59:22.222780"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grp_metrics = [var for var in metrics2 if var in d.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.275217",
     "start_time": "2016-08-17T07:59:22.244289"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# center variables by group\n",
    "bygrp = d.loc[:, grp_metrics]\n",
    "bygrp = bygrp.groupby('pd_l1').transform(lambda x: x - x.mean())\n",
    "bygrp['patient_id'] = d.patient_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.301832",
     "start_time": "2016-08-17T07:59:22.276693"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge recentered variables back into original dataframe\n",
    "df = pd.merge(d, bygrp, on = 'patient_id', suffixes = ['', '_centered_by_pd_l1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T07:59:22.420738",
     "start_time": "2016-08-17T07:59:22.356618"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## prep dflong_pfs which will be used for survival analysis using stan\n",
    "df_long_pfs = survivalstan.prep_data_long_surv(df = df,\n",
    "                                               event_col = 'is_progressed_or_deceased',\n",
    "                                               time_col = 'pfs')\n",
    "## prep dflong_pfs which will be used for survival analysis using stan\n",
    "df_long_os = survivalstan.prep_data_long_surv(df = df,\n",
    "                                               event_col = 'is_deceased',\n",
    "                                               time_col = 'os')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# multivariate analysis using varying-coef model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-17T08:27:09.524216",
     "start_time": "2016-08-17T08:27:09.501719"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../utils/stan/logistic_model.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_expressed_missense_and_neoant_mutations.stan\n",
      "../utils/stan/logistic_model_by_group.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef_hsprior.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_alt.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs3.stan\n",
      "../utils/stan/pem_survival_model_randomwalk.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_rate_only.stan\n",
      "../utils/stan/pem_survival_model_gamma.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_missense_and_neoant_rates.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs4.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline.stan\n",
      "../utils/stan/pem_survival_model_randomwalk2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline2.stan\n"
     ]
    }
   ],
   "source": [
    "models = survivalstan.utils.read_files('../utils/stan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "survstan_pfs_varcoef = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df_long_pfs,\n",
    "    model_code = models['pem_survival_model_unstructured_varcoef.stan'],\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    sample_col = 'patient_id',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    grp_coef_type = 'vector-of-vectors',\n",
    "    seed = seed,\n",
    "    )\n",
    "survstan_os_varcoef = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df_long_os,\n",
    "    model_code = models['pem_survival_model_unstructured_varcoef.stan'],\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    sample_col = 'patient_id',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    grp_coef_type = 'vector-of-vectors',\n",
    "    seed = seed,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survstan_pfs_varcoef2 = functools.partial(\n",
    "    survivalstan.fit_stan_survival_model,\n",
    "    df = df_long_pfs,\n",
    "    model_code = models['pem_survival_model_unstructured_varcoef_hsprior.stan'],\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    sample_col = 'patient_id',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    grp_coef_type = 'vector-of-vectors',\n",
    "    seed = seed,\n",
    "    stan_data = {'nu': 1} \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## multivariate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 297.302 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tavi/miniconda2/lib/python2.7/site-packages/stanity/psis.py:228: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  elif sort == 'in-place':\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/stanity/psis.py:246: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  bs /= 3 * x[sort[np.floor(n/4 + 0.5) - 1]]\n"
     ]
    }
   ],
   "source": [
    "formula = 'liver_mets + {}_centered_by_pd_l1 + {}_centered_by_pd_l1 + {}_centered_by_pd_l1'.format(\n",
    "    'log_missense_snv_count',\n",
    "     'log_peripheral_clonality_a',\n",
    "     'log_tcell_fraction',\n",
    "    )\n",
    "multivariate_models_varcoef = list()\n",
    "multivariate_models_varcoef.append(survstan_pfs_varcoef(formula = formula,\n",
    "                                                        model_cohort = formula,\n",
    "                                                        group_col='pd_l1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran in 408.986 sec.\n"
     ]
    }
   ],
   "source": [
    "multivariate_models_varcoef_os = list()\n",
    "multivariate_models_varcoef_os.append(survstan_os_varcoef(formula = formula,\n",
    "                                                        model_cohort = formula,\n",
    "                                                        group_col='pd_l1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarize results from MV model for PFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAGHCAYAAAA9Xt2dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiEqj/B3b50NTqSS+7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76b0b1c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data=multivariate_models_varcoef[0]['grp_coefs'],\n",
    "           x='value',\n",
    "           y='variable',\n",
    "           hue='group',\n",
    "           fliersize=0,\n",
    "           whis=[2.5, 97.5])\n",
    "_ = plt.xlim([-10, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{mv_model_by_pdl1}}}\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABO0AAAGHCAYAAADsjKaFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a4974d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot HRs (`exp(beta)`), formatted extra-wide for the manuscript\n",
    "paper.hyper_figure_label_printer('mv_model_by_pdl1')\n",
    "## make this plot much wider than usual\n",
    "current_fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "new_fig_size = [current_fig_size[0]*3, current_fig_size[1]]\n",
    "plt.rcParams[\"figure.figsize\"] = new_fig_size\n",
    "## plot\n",
    "sb.boxplot(data=multivariate_models_varcoef[0]['grp_coefs'],\n",
    "           x='exp(beta)',\n",
    "           y='variable',\n",
    "           hue='group',\n",
    "           fliersize=0,\n",
    "           whis=[2.5, 97.5], \n",
    "          )\n",
    "_ = plt.xlim([0, 30])\n",
    "_ = plt.vlines(1, -10, 10, linestyles='--')\n",
    "_ = plt.ylabel('')\n",
    "#_ = plt.title('Hazard associated with log(Peripheral clonality at timepoint A) \\n by level of intratumoral PD-L1 expression')\n",
    "_ = plt.xlabel('Hazard Ratio for {}'.format(cohort.hazard_plot_name))\n",
    "_ = plt.yticks([0, 1, 2, 3],\n",
    "               ['Liver Metastasis',\n",
    "                'log(# Missense SNVs\\n/ MB)',\n",
    "                'log({tcr_peripheral_a_clonality_short_plot_name})'.format(tcr_peripheral_a_clonality_short_plot_name=cohort.tcr_peripheral_a_clonality_short_plot_name),\n",
    "                'log({til_fraction_plot_name})'.format(til_fraction_plot_name=cohort.til_fraction_plot_name),\n",
    "               ])\n",
    "## revert width to original size\n",
    "plt.rcParams[\"figure.figsize\"] = current_fig_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>group</th>\n",
       "      <th>model_cohort</th>\n",
       "      <th>ic0_value</th>\n",
       "      <th>value</th>\n",
       "      <th>exp(beta)</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>IC0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-3.714103</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>IC1</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-3.714103</td>\n",
       "      <td>-2.458640</td>\n",
       "      <td>0.085551</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>IC2</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-3.714103</td>\n",
       "      <td>-2.264194</td>\n",
       "      <td>0.103914</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>IC0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-4.428929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>IC1</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-4.428929</td>\n",
       "      <td>-1.234443</td>\n",
       "      <td>0.290997</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter group                                       model_cohort  ic0_value  \\\n",
       "0     0   IC0  liver_mets + log_missense_snv_count_centered_b...  -3.714103   \n",
       "1     0   IC1  liver_mets + log_missense_snv_count_centered_b...  -3.714103   \n",
       "2     0   IC2  liver_mets + log_missense_snv_count_centered_b...  -3.714103   \n",
       "3     1   IC0  liver_mets + log_missense_snv_count_centered_b...  -4.428929   \n",
       "4     1   IC1  liver_mets + log_missense_snv_count_centered_b...  -4.428929   \n",
       "\n",
       "      value  exp(beta)   variable  \n",
       "0  0.000000   1.000000  intercept  \n",
       "1 -2.458640   0.085551  intercept  \n",
       "2 -2.264194   0.103914  intercept  \n",
       "3  0.000000   1.000000  intercept  \n",
       "4 -1.234443   0.290997  intercept  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## prep 'beta' coefficients for IC groups\n",
    "## so that this can be communicated as well\n",
    "grp_alpha = survivalstan.utils.extract_params_long([multivariate_models_varcoef[0]],\n",
    "                                                   element='grp_alpha',\n",
    "                                                   varnames = ['IC0','IC1','IC2'])\n",
    "ic0_values = grp_alpha.loc[grp_alpha['variable']=='IC0',['iter','value','model_cohort']]\n",
    "ic0_values.rename(columns={'value': 'ic0_value'}, inplace=True)\n",
    "grp_alpha = grp_alpha.merge(ic0_values, on=['iter','model_cohort'])\n",
    "grp_alpha['beta'] = grp_alpha.apply(lambda row: row['value'] - row['ic0_value'], axis=1)\n",
    "grp_alpha['exp(beta)'] = np.exp(grp_alpha['beta'])\n",
    "grp_alpha.drop('value', axis=1, inplace=True)\n",
    "grp_alpha.rename(columns = {'variable': 'group', 'beta': 'value'}, inplace=True)\n",
    "grp_alpha['variable'] = 'intercept'\n",
    "grp_alpha.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76a4109910>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAGHCAYAAAB8sB+WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMI64AACMIy4AAOOICwDAOOICADDu/wGT0pVoA6StRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a4da8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data = grp_alpha,\n",
    "           x = 'value',\n",
    "           y = 'group',\n",
    "           fliersize = 0,\n",
    "           whis = [2.5, 97.5],\n",
    "          )\n",
    "\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{mv_model_pdl1_coefs}}}\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbAAAAGHCAYAAADRMQc/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHe7v8cauAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a526b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paper.hyper_figure_label_printer('mv_model_pdl1_coefs')\n",
    "sb.boxplot(data = grp_alpha,\n",
    "           x = 'exp(beta)',\n",
    "           y = 'group',\n",
    "           fliersize = 0,\n",
    "           whis = [2.5, 97.5],\n",
    "          )\n",
    "_ = plt.xlim([0, 2])\n",
    "_ = plt.vlines(1, -10, 10, linestyles='--')\n",
    "_ = plt.ylabel('Intratumoral PD-L1 Expression\\n(IC Grade)')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exp(beta)</th>\n",
       "      <th>group</th>\n",
       "      <th>ic0_value</th>\n",
       "      <th>iter</th>\n",
       "      <th>model_cohort</th>\n",
       "      <th>value</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>IC0</td>\n",
       "      <td>-3.714103</td>\n",
       "      <td>0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.085551</td>\n",
       "      <td>IC1</td>\n",
       "      <td>-3.714103</td>\n",
       "      <td>0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-2.458640</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.103914</td>\n",
       "      <td>IC2</td>\n",
       "      <td>-3.714103</td>\n",
       "      <td>0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-2.264194</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>IC0</td>\n",
       "      <td>-4.428929</td>\n",
       "      <td>1</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.290997</td>\n",
       "      <td>IC1</td>\n",
       "      <td>-4.428929</td>\n",
       "      <td>1</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-1.234443</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   exp(beta) group  ic0_value  iter  \\\n",
       "0   1.000000   IC0  -3.714103     0   \n",
       "1   0.085551   IC1  -3.714103     0   \n",
       "2   0.103914   IC2  -3.714103     0   \n",
       "3   1.000000   IC0  -4.428929     1   \n",
       "4   0.290997   IC1  -4.428929     1   \n",
       "\n",
       "                                        model_cohort     value   variable  \n",
       "0  liver_mets + log_missense_snv_count_centered_b...  0.000000  intercept  \n",
       "1  liver_mets + log_missense_snv_count_centered_b... -2.458640  intercept  \n",
       "2  liver_mets + log_missense_snv_count_centered_b... -2.264194  intercept  \n",
       "3  liver_mets + log_missense_snv_count_centered_b...  0.000000  intercept  \n",
       "4  liver_mets + log_missense_snv_count_centered_b... -1.234443  intercept  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_coefs = pd.concat([grp_alpha, multivariate_models_varcoef[0]['grp_coefs']])\n",
    "all_coefs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{mv_model_incl_pdl1_coefs}}}\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAGHCAYAAACplLYqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREpIH/A7Ww0KJ2xiefAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a433ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paper.hyper_figure_label_printer('mv_model_incl_pdl1_coefs')\n",
    "sb.boxplot(data = all_coefs,\n",
    "           x = 'exp(beta)',\n",
    "           y = 'variable',\n",
    "           hue = 'group',\n",
    "           fliersize = 0,\n",
    "           whis = [2.5, 97.5],\n",
    "          )\n",
    "_ = plt.xlim([0, 10])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.vlines(1, -10, 10, linestyles='--')\n",
    "_ = plt.yticks([0, 1, 2, 3, 4],\n",
    "               ['Intercept (PD-L1 Effect)',\n",
    "                'Liver Metastasis',\n",
    "                'log(# Missense SNVs\\n/ MB)',\n",
    "                'log({tcr_peripheral_a_clonality_short_plot_name})'.format(tcr_peripheral_a_clonality_short_plot_name=cohort.tcr_peripheral_a_clonality_short_plot_name),\n",
    "                'log({til_fraction_plot_name})'.format(til_fraction_plot_name=cohort.til_fraction_plot_name),\n",
    "                ])\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f76a4fecad0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHUAAAFRCAYAAAD6l5GqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDHFQh4iIiIiIiIioGfo/Scj50co/FzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767d001290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lmplot(data = df, x='peripheral_clonality_a', y='log_missense_snv_count', col='pd_l1',\n",
    "          hue='benefit', logistic=True, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f767cf506d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHUAAAFRCAYAAAD6l5GqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERMSGtKkjIiIiIiIiImJD/wNLBnZ+PkjpKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767cb1a0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lmplot(data = df, x='tcell_fraction', y='log_missense_snv_count', col='pd_l1',\n",
    "          hue='benefit', logistic=True, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_hyper_label(row):\n",
    "    return 'm0pfs_{}_{}'.format(row['variable'], row['group'])\n",
    "\n",
    "all_coefs['hyper_label'] = all_coefs.apply(lambda row: make_hyper_label(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{m0pfs_intercept_IC0:HR=1.00, 95% CI (1.00, 1.00)}}}\n",
      "{{{m0pfs_intercept_IC1:HR=0.29, 95% CI (0.079, 1.36)}}}\n",
      "{{{m0pfs_intercept_IC2:HR=0.083, 95% CI (0.0087, 0.55)}}}\n",
      "{{{m0pfs_liver_mets_IC0:HR=4.17, 95% CI (0.53, 33.98)}}}\n",
      "{{{m0pfs_liver_mets_IC1:HR=4.27, 95% CI (1.15, 15.08)}}}\n",
      "{{{m0pfs_liver_mets_IC2:HR=3.80, 95% CI (0.79, 22.70)}}}\n",
      "{{{m0pfs_log_missense_snv_count_centered_by_pd_l1_IC0:HR=1.00, 95% CI (0.49, 2.37)}}}\n",
      "{{{m0pfs_log_missense_snv_count_centered_by_pd_l1_IC1:HR=0.76, 95% CI (0.50, 1.16)}}}\n",
      "{{{m0pfs_log_missense_snv_count_centered_by_pd_l1_IC2:HR=0.53, 95% CI (0.33, 0.83)}}}\n",
      "{{{m0pfs_log_peripheral_clonality_a_centered_by_pd_l1_IC0:HR=1.51, 95% CI (0.32, 7.22)}}}\n",
      "{{{m0pfs_log_peripheral_clonality_a_centered_by_pd_l1_IC1:HR=1.09, 95% CI (0.32, 3.46)}}}\n",
      "{{{m0pfs_log_peripheral_clonality_a_centered_by_pd_l1_IC2:HR=50.87, 95% CI (2.99, 1486.55)}}}\n",
      "{{{m0pfs_log_tcell_fraction_centered_by_pd_l1_IC0:HR=0.12, 95% CI (0.0028, 3.46)}}}\n",
      "{{{m0pfs_log_tcell_fraction_centered_by_pd_l1_IC1:HR=0.11, 95% CI (0.0077, 1.20)}}}\n",
      "{{{m0pfs_log_tcell_fraction_centered_by_pd_l1_IC2:HR=0.078, 95% CI (0.0051, 1.07)}}}\n"
     ]
    }
   ],
   "source": [
    "for name, group in all_coefs.groupby('hyper_label'):\n",
    "    paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label=name, series=group['exp(beta)'], summary='median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### summarize results from MV model for OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exp(beta)</th>\n",
       "      <th>group</th>\n",
       "      <th>ic0_value</th>\n",
       "      <th>iter</th>\n",
       "      <th>model_cohort</th>\n",
       "      <th>value</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>IC0</td>\n",
       "      <td>-7.510203</td>\n",
       "      <td>0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.347772</td>\n",
       "      <td>IC1</td>\n",
       "      <td>-7.510203</td>\n",
       "      <td>0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-1.056209</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.093100</td>\n",
       "      <td>IC2</td>\n",
       "      <td>-7.510203</td>\n",
       "      <td>0</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-2.374077</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>IC0</td>\n",
       "      <td>-5.899888</td>\n",
       "      <td>1</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.108291</td>\n",
       "      <td>IC1</td>\n",
       "      <td>-5.899888</td>\n",
       "      <td>1</td>\n",
       "      <td>liver_mets + log_missense_snv_count_centered_b...</td>\n",
       "      <td>-2.222937</td>\n",
       "      <td>intercept</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   exp(beta) group  ic0_value  iter  \\\n",
       "0   1.000000   IC0  -7.510203     0   \n",
       "1   0.347772   IC1  -7.510203     0   \n",
       "2   0.093100   IC2  -7.510203     0   \n",
       "3   1.000000   IC0  -5.899888     1   \n",
       "4   0.108291   IC1  -5.899888     1   \n",
       "\n",
       "                                        model_cohort     value   variable  \n",
       "0  liver_mets + log_missense_snv_count_centered_b...  0.000000  intercept  \n",
       "1  liver_mets + log_missense_snv_count_centered_b... -1.056209  intercept  \n",
       "2  liver_mets + log_missense_snv_count_centered_b... -2.374077  intercept  \n",
       "3  liver_mets + log_missense_snv_count_centered_b...  0.000000  intercept  \n",
       "4  liver_mets + log_missense_snv_count_centered_b... -2.222937  intercept  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize coefficients for model with OS as outcome\n",
    "grp_alpha_os = survivalstan.utils.extract_params_long([multivariate_models_varcoef_os[0]],\n",
    "                                                   element='grp_alpha',\n",
    "                                                   varnames = ['IC0','IC1','IC2'])\n",
    "ic0_values_os = grp_alpha_os.loc[grp_alpha_os['variable']=='IC0',['iter','value','model_cohort']]\n",
    "ic0_values_os.rename(columns={'value': 'ic0_value'}, inplace=True)\n",
    "grp_alpha_os = grp_alpha_os.merge(ic0_values_os, on=['iter','model_cohort'])\n",
    "grp_alpha_os['beta'] = grp_alpha_os.apply(lambda row: row['value'] - row['ic0_value'], axis=1)\n",
    "grp_alpha_os['exp(beta)'] = np.exp(grp_alpha_os['beta'])\n",
    "grp_alpha_os.drop('value', axis=1, inplace=True)\n",
    "grp_alpha_os.rename(columns = {'variable': 'group', 'beta': 'value'}, inplace=True)\n",
    "grp_alpha_os['variable'] = 'intercept'\n",
    "all_coefs_os = pd.concat([grp_alpha_os, multivariate_models_varcoef_os[0]['grp_coefs']])\n",
    "all_coefs_os.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{mv_model_incl_pdl1_coefs_os}}}\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAGHCAYAAACplLYqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIg38P1nu19RQwj6kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f767c990210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paper.hyper_figure_label_printer('mv_model_incl_pdl1_coefs_os')\n",
    "sb.boxplot(data = all_coefs_os,\n",
    "           x = 'exp(beta)',\n",
    "           y = 'variable',\n",
    "           hue = 'group',\n",
    "           fliersize = 0,\n",
    "           whis = [2.5, 97.5],\n",
    "          )\n",
    "_ = plt.xlim([0, 10])\n",
    "_ = plt.ylabel('')\n",
    "_ = plt.vlines(1, -10, 10, linestyles='--')\n",
    "_ = plt.yticks([0, 1, 2, 3, 4],\n",
    "               ['Intercept (PD-L1 Effect)',\n",
    "                'Liver Metastasis',\n",
    "                'log(# Missense SNVs\\n/ MB)',\n",
    "                'log({tcr_peripheral_a_clonality_short_plot_name})'.format(tcr_peripheral_a_clonality_short_plot_name=cohort.tcr_peripheral_a_clonality_short_plot_name),\n",
    "                'log({til_fraction_plot_name})'.format(til_fraction_plot_name=cohort.til_fraction_plot_name),\n",
    "                ])\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{m0os_intercept_IC0:HR=1.00, 95% CI (1.00, 1.00)}}}\n",
      "{{{m0os_intercept_IC1:HR=0.23, 95% CI (0.054, 1.10)}}}\n",
      "{{{m0os_intercept_IC2:HR=0.023, 95% CI (0.00047, 0.30)}}}\n",
      "{{{m0os_liver_mets_IC0:HR=4.78, 95% CI (0.58, 53.10)}}}\n",
      "{{{m0os_liver_mets_IC1:HR=4.11, 95% CI (0.91, 16.01)}}}\n",
      "{{{m0os_liver_mets_IC2:HR=3.73, 95% CI (0.61, 24.52)}}}\n",
      "{{{m0os_log_missense_snv_count_centered_by_pd_l1_IC0:HR=1.26, 95% CI (0.60, 3.19)}}}\n",
      "{{{m0os_log_missense_snv_count_centered_by_pd_l1_IC1:HR=0.83, 95% CI (0.50, 1.28)}}}\n",
      "{{{m0os_log_missense_snv_count_centered_by_pd_l1_IC2:HR=0.56, 95% CI (0.33, 1.00)}}}\n",
      "{{{m0os_log_peripheral_clonality_a_centered_by_pd_l1_IC0:HR=1.08, 95% CI (0.21, 4.15)}}}\n",
      "{{{m0os_log_peripheral_clonality_a_centered_by_pd_l1_IC1:HR=1.09, 95% CI (0.33, 3.81)}}}\n",
      "{{{m0os_log_peripheral_clonality_a_centered_by_pd_l1_IC2:HR=4895.08, 95% CI (80.81, 10374151.41)}}}\n",
      "{{{m0os_log_tcell_fraction_centered_by_pd_l1_IC0:HR=0.053, 95% CI (0.00053, 11.87)}}}\n",
      "{{{m0os_log_tcell_fraction_centered_by_pd_l1_IC1:HR=0.047, 95% CI (0.0018, 0.68)}}}\n",
      "{{{m0os_log_tcell_fraction_centered_by_pd_l1_IC2:HR=0.026, 95% CI (0.00093, 0.40)}}}\n"
     ]
    }
   ],
   "source": [
    "def make_hyper_label(row):\n",
    "    return 'm0os_{}_{}'.format(row['variable'], row['group'])\n",
    "\n",
    "all_coefs_os['hyper_label'] = all_coefs_os.apply(lambda row: make_hyper_label(row), axis=1)\n",
    "for name, group in all_coefs_os.groupby('hyper_label'):\n",
    "    paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label=name, series=group['exp(beta)'], summary='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{liver_met_plot_name:Liver Metastasis}}}\n",
      "{{{missense_snv_count_plot_name:# Missense SNVs / MB}}}\n",
      "{{{tcr_peripheral_a_clonality_short_plot_name:Pre-treatment Clonality}}}\n",
      "{{{til_fraction_plot_name:TIL Proportion}}}\n"
     ]
    }
   ],
   "source": [
    "def print_string(string):\n",
    "    return \"%s\" % (string)\n",
    "paper.hyper_label_printer(formatter=print_string, label='liver_met_plot_name', string='Liver Metastasis')\n",
    "paper.hyper_label_printer(formatter=print_string, label='missense_snv_count_plot_name', string='# Missense SNVs / MB')\n",
    "paper.hyper_label_printer(formatter=print_string, label='tcr_peripheral_a_clonality_short_plot_name', string=cohort.tcr_peripheral_a_clonality_short_plot_name)\n",
    "paper.hyper_label_printer(formatter=print_string, label='til_fraction_plot_name', string=cohort.til_fraction_plot_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "name": "TCR Clonality.ipynb",
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "877px",
   "left": "0px",
   "right": "2073px",
   "top": "106px",
   "width": "538px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
